Mo’ Data, Mo’ Insights

The premise seems to make sense on the surface: if having “some” data helps life sciences companies develop insights that improve their businesses, having access to Big Data from multiple sources (patient data, internal sales data, payer prescription and claims data, lab data, etc.) as they do today should lead to many more and much better insights.

Unfortunately, that’s not what’s actually happening. In fact, more often than not access to more data is having the opposite effect.

When disparate data sources across multiple departments and even organizations grow to a TB or larger it becomes difficult to connect and manage them in a scalable way. The information is there, but making cross-functional use of it to understand the pulse of the business becomes more challenging.

Essentially, it’s like trying to build a high-performance racecar without any bolts, screws, or welding capabilities. All the pieces are there, and they’re each excellent on their own. But there’s no way to join them together so the vehicle can roll out the door in a form a car buyer would want to purchase.

This is the problem next-generation analytics are being designed to solve. They are able to take all of these disparate data sources, including previously unstructured data, and make it accessible in a way that enables life sciences companies to gain a 360-degree understanding of brand performance as it relates to individuals and populations. These are not just simple, retrospective analyses that show the actions patients and populations have taken, but predictive and behavioral analytics that delve much deeper to uncover the motivations behind these actions, helping to predict why they are happening and how to engage in the future.

Through these insights, life sciences companies gain a better understanding of how to interact with patients more effectively to improve compliance (and ultimately outcomes) while driving down the cost per patient. This information can then be shared with payers and healthcare professionals to create a true risk-sharing dialog and collaborative approach to meet the requirements of value-based care. One that is far more effective at keeping patients healthy than simply telling them to “walk more.”

Creating “aha” moments

The goal of any business analytics program in any industry is to discover “aha” moments that provide guidance on how to make changes that result in meaningful performance improvements. In healthcare, however, these improvements are not focused solely on the bottom line; they have a direct impact on the quality and often the length of life of the people life sciences companies serve.

These revelations do not have to be earth-shaking. To the contrary, it’s often little changes here or a slight course correction there that can have a profound long-term effect on the success of a prescribed care plan. When you apply predictive modeling and true outcome-based data to the real world, the “aha” moments often reveal themselves in the form of common sense scenarios.

Here’s an example of how mo’ data led to mo’ insights. After an organization created a series of carefully designed, patient-centric care models based on established best practices, it noticed that drug compliance was extremely low in certain areas of New York City. That would have been the extent of its knowledge if the organization had been using conventional data analytics. It would have had to use a lengthy “trial and error” method to determine what changes to make to improve compliance in those areas.

With next-generation analytics at its disposal, however, this organization was able to use behavioral and geographic data to get right to the root cause. The problem was two-pronged.

The first contributing factor was that behavioral data shows many people in New York live in high-rise, multiple-unit buildings. The second factor was geographic data showed that receiving medication from a mail order pharmacy was resulting in packages too large to fit into the typical high-rise mail slot. As a result, packages were being left on the floor (subject to theft) or returned to the post office, making it difficult and time-consuming to pick them up.

With this understanding, the organization was able to redirect those patients to a pharmacy close to where they live and/or work. By leveraging the insights and having transparency into what caused the issue, compliance increased as a result to levels comparable with the rest of the same patient cohort.

Making the leap

That is the “what” and the “why.” What’s left is the “how” – as in how to get to this point.

The first thing life sciences companies must do is accept that their current infrastructure and capabilities may not allow them to be able to take advantage of the wider world of data available to them today. The good news is many have shifted their IT function to be more partner- and cloud-centric than internal hardware-centric.

They have come to realize the cloud is more scalable and accessible than internal hardware, and thus better-suited to dealing with large volumes of data from many disparate sources. In fact, certain necessary functions such as normalizing data are already built into cloud provider offerings, eliminating the need for IT to manage them.

The problem with the partner approach is many life sciences companies get caught up in large-scale consulting arrangements that ultimately just tell them what they already know – that they must adapt to the new world. That is probably the opposite of the “aha” moment we talked about earlier.

What life sciences companies need to focus on, instead, is the execution – rather than making analytics an IT function, they should look to offload technical management to the cloud and build a dedicated team that focuses on developing insights. That team can be composed of internal business users, external resources or a combination of the two.

Whatever the makeup of the team, the key selection criteria should be domain expertise rather than technical prowess. They will be the ones closest to the issues, and to payers and healthcare professionals. Putting this type of broad-ranging analytical power into their hands will help drive better, more actionable insights that solve real-world problems – and yield real-world benefits.

Getting started

The lowest hanging fruit for improving commercial effectiveness is creating a more detailed picture of critical process systems in care and compliance while incorporating multiple factors such as risk score, socio-economic and demographic details along with claims and brand data. This information can then be shared with healthcare professionals to educate them about the opportunities and impact of a particular drug on their population.

This same information can be used to improve how healthcare professionals are targeted. Rather than using routine total prescription deciling, life sciences companies can use risk scores to ensure that products and programs are being put in front of the healthcare professionals who have the greatest need for them. Once the compliance rates of a product within a class or across classes are shared, a targeted co-pay assistance or education program can be deployed rather than sending the same generic messages to all areas.

Using this level of precision not only gets the right products in front of the right healthcare professionals at the right time. It conditions them to prioritize messages from the life sciences company because they are always specific. And relevant.

Doing more with mo’

While most people generally believe more is always better, when it comes to data that isn’t necessarily the case. It’s how the data is used, and what it’s used to accomplish, that makes the difference.

Next-generation analytics can help life sciences companies take advantage of the current proliferation of Big Data from many sources and use it to gain more precise, higher-quality insights that simply weren’t available before. All of which will make those who adopt it a much more valuable partner to payers and healthcare professionals.

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About the authors

John Pagliuca is Vice President of Life Sciences at SCIO Health Analytics, where he leads the commercialization efforts of SCIO’s SaaS solution suite and advanced analytics within the US Life Science market. He has more than 15 years of sales, marketing and technology experience in quantitative analytics and SaaS solutions for Life Sciences.

Mark Feeney is a life sciences consultant at SCIO Health Analytics with more than 20 years of experience working with physician influence networks and key life science opinion leaders on resource deployment, , sales force strategies, geo-marketing analysis, and the use of patient-level longitudinal and prescriber Rx data.